class_docstr: Perceptron function
  
  Parameters
  ----------
  x: np.ndarray
      the input data
  y: np.ndarray
      the data to predict from the input data
docstr: Apply the perceptron transform to x using the provided parameters
  
  Parameters
  ----------
  parameters: ArrayLike
      parameters of the perceptron
  
  Returns
  -------
  np.ndarray
      transformed data
docstr_tok: ["Apply","the","perceptron","transform","to","x","using","the","provided","parameters","Parameters","--","--","--","--","--","parameters",":","ArrayLike","parameters","of","the","perceptron","Returns","--","--","--","-","np.ndarray","transformed","data"]
code: def apply(self, parameters: tp.ArrayLike) -> np.ndarray:
      """Apply the perceptron transform to x using the provided parameters
  
      Parameters
      ----------
      parameters: ArrayLike
          parameters of the perceptron
  
      Returns
      -------
      np.ndarray
          transformed data
      """
      parameters = np.array(parameters, copy=False)
      assert parameters.shape == (10,)
      tmp = np.tanh(
          self._x[:, None] * parameters[None, :3]
          + parameters[None, 3:6]
      )
      tmp *= parameters[None, 6:9]
      output: np.ndarray = np.sum(tmp, axis=1) + parameters[-1]
      return output
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 3 for calling the Perceptron obj's underlying member method code with example usage: obj.apply(parameters) and returns: output